Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.0 KiB
Average record size in memory223.2 B

Variable types

Numeric8
Categorical2
Boolean1

Alerts

Sleep_Hours is highly overall correlated with Stress_LevelHigh correlation
Stress_Level is highly overall correlated with Sleep_HoursHigh correlation
Bugs has 11 (2.2%) zerosZeros
Coffee_Cups has 47 (9.4%) zerosZeros
Meetings has 19 (3.8%) zerosZeros
Interruptions has 45 (9.0%) zerosZeros

Reproduction

Analysis started2026-01-20 17:39:45.009283
Analysis finished2026-01-20 17:39:53.651398
Duration8.64 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Hours_Worked
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.504
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:53.758723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median10
Q313
95-th percentile15
Maximum15
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5433168
Coefficient of variation (CV)0.37282374
Kurtosis-1.2138252
Mean9.504
Median Absolute Deviation (MAD)3
Skewness0.0096481014
Sum4752
Variance12.555094
MonotonicityNot monotonic
2026-01-20T17:39:53.882895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1556
11.2%
451
10.2%
647
9.4%
1046
9.2%
1143
8.6%
1340
8.0%
1239
7.8%
839
7.8%
938
7.6%
738
7.6%
Other values (2)63
12.6%
ValueCountFrequency (%)
451
10.2%
533
6.6%
647
9.4%
738
7.6%
839
7.8%
938
7.6%
1046
9.2%
1143
8.6%
1239
7.8%
1340
8.0%
ValueCountFrequency (%)
1556
11.2%
1430
6.0%
1340
8.0%
1239
7.8%
1143
8.6%
1046
9.2%
938
7.6%
839
7.8%
738
7.6%
647
9.4%

Sleep_Hours
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.436
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:54.007278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum8
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7063785
Coefficient of variation (CV)0.31390332
Kurtosis-1.3037357
Mean5.436
Median Absolute Deviation (MAD)2
Skewness0.008948787
Sum2718
Variance2.9117275
MonotonicityNot monotonic
2026-01-20T17:39:54.128109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
796
19.2%
489
17.8%
389
17.8%
680
16.0%
575
15.0%
871
14.2%
ValueCountFrequency (%)
389
17.8%
489
17.8%
575
15.0%
680
16.0%
796
19.2%
871
14.2%
ValueCountFrequency (%)
871
14.2%
796
19.2%
680
16.0%
575
15.0%
489
17.8%
389
17.8%

Bugs
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.15
Minimum0
Maximum50
Zeros11
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:54.324948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median26
Q337
95-th percentile49
Maximum50
Range50
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.909866
Coefficient of variation (CV)0.59283762
Kurtosis-1.1765418
Mean25.15
Median Absolute Deviation (MAD)13
Skewness-0.038445849
Sum12575
Variance222.30411
MonotonicityNot monotonic
2026-01-20T17:39:54.525503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5019
 
3.8%
3918
 
3.6%
2515
 
3.0%
3215
 
3.0%
3614
 
2.8%
1014
 
2.8%
2814
 
2.8%
113
 
2.6%
2213
 
2.6%
513
 
2.6%
Other values (41)352
70.4%
ValueCountFrequency (%)
011
2.2%
113
2.6%
27
1.4%
310
2.0%
410
2.0%
513
2.6%
69
1.8%
711
2.2%
810
2.0%
911
2.2%
ValueCountFrequency (%)
5019
3.8%
497
 
1.4%
4811
2.2%
474
 
0.8%
4612
2.4%
458
1.6%
4410
2.0%
437
 
1.4%
429
1.8%
416
 
1.2%

Deadline_Days
Real number (ℝ)

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.676
Minimum0
Maximum60
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:54.698723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q116
median31
Q345
95-th percentile58
Maximum60
Range60
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.284691
Coefficient of variation (CV)0.56345974
Kurtosis-1.1588418
Mean30.676
Median Absolute Deviation (MAD)15
Skewness-0.0064872904
Sum15338
Variance298.76055
MonotonicityNot monotonic
2026-01-20T17:39:54.856166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5819
 
3.8%
3315
 
3.0%
2815
 
3.0%
1613
 
2.6%
2412
 
2.4%
1212
 
2.4%
3412
 
2.4%
4611
 
2.2%
5511
 
2.2%
3111
 
2.2%
Other values (51)369
73.8%
ValueCountFrequency (%)
04
0.8%
15
1.0%
29
1.8%
38
1.6%
48
1.6%
58
1.6%
67
1.4%
75
1.0%
89
1.8%
97
1.4%
ValueCountFrequency (%)
606
 
1.2%
597
 
1.4%
5819
3.8%
578
1.6%
567
 
1.4%
5511
2.2%
546
 
1.2%
536
 
1.2%
527
 
1.4%
516
 
1.2%

Coffee_Cups
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.922
Minimum0
Maximum10
Zeros47
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:54.955981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1552857
Coefficient of variation (CV)0.64105763
Kurtosis-1.2359633
Mean4.922
Median Absolute Deviation (MAD)3
Skewness-0.0060374568
Sum2461
Variance9.9558277
MonotonicityNot monotonic
2026-01-20T17:39:55.042369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
153
10.6%
949
9.8%
849
9.8%
548
9.6%
047
9.4%
446
9.2%
744
8.8%
644
8.8%
343
8.6%
240
8.0%
ValueCountFrequency (%)
047
9.4%
153
10.6%
240
8.0%
343
8.6%
446
9.2%
548
9.6%
644
8.8%
744
8.8%
849
9.8%
949
9.8%
ValueCountFrequency (%)
1037
7.4%
949
9.8%
849
9.8%
744
8.8%
644
8.8%
548
9.6%
446
9.2%
343
8.6%
240
8.0%
153
10.6%

Meetings
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.164
Minimum0
Maximum20
Zeros19
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:55.149292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q316
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.2275733
Coefficient of variation (CV)0.61270891
Kurtosis-1.2743231
Mean10.164
Median Absolute Deviation (MAD)6
Skewness0.0065174622
Sum5082
Variance38.782669
MonotonicityNot monotonic
2026-01-20T17:39:55.262070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2031
 
6.2%
730
 
6.0%
1930
 
6.0%
328
 
5.6%
1228
 
5.6%
127
 
5.4%
226
 
5.2%
1625
 
5.0%
1324
 
4.8%
424
 
4.8%
Other values (11)227
45.4%
ValueCountFrequency (%)
019
3.8%
127
5.4%
226
5.2%
328
5.6%
424
4.8%
522
4.4%
619
3.8%
730
6.0%
821
4.2%
921
4.2%
ValueCountFrequency (%)
2031
6.2%
1930
6.0%
1821
4.2%
1724
4.8%
1625
5.0%
1520
4.0%
1422
4.4%
1324
4.8%
1228
5.6%
1120
4.0%

Interruptions
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.968
Minimum0
Maximum10
Zeros45
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:55.365208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0747169
Coefficient of variation (CV)0.61890436
Kurtosis-1.1744368
Mean4.968
Median Absolute Deviation (MAD)3
Skewness-0.016273695
Sum2484
Variance9.4538838
MonotonicityNot monotonic
2026-01-20T17:39:55.452665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
854
10.8%
754
10.8%
251
10.2%
348
9.6%
448
9.6%
545
9.0%
045
9.0%
642
8.4%
938
7.6%
1038
7.6%
ValueCountFrequency (%)
045
9.0%
137
7.4%
251
10.2%
348
9.6%
448
9.6%
545
9.0%
642
8.4%
754
10.8%
854
10.8%
938
7.6%
ValueCountFrequency (%)
1038
7.6%
938
7.6%
854
10.8%
754
10.8%
642
8.4%
545
9.0%
448
9.6%
348
9.6%
251
10.2%
137
7.4%

Experience_Years
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
Senior
187 
Mid
177 
Junior
136 

Length

Max length6
Median length6
Mean length4.938
Min length3

Characters and Unicode

Total characters2469
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSenior
2nd rowJunior
3rd rowJunior
4th rowMid
5th rowJunior

Common Values

ValueCountFrequency (%)
Senior187
37.4%
Mid177
35.4%
Junior136
27.2%

Length

2026-01-20T17:39:55.556799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T17:39:55.640760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
senior187
37.4%
mid177
35.4%
junior136
27.2%

Most occurring characters

ValueCountFrequency (%)
i500
20.3%
n323
13.1%
o323
13.1%
r323
13.1%
e187
 
7.6%
S187
 
7.6%
M177
 
7.2%
d177
 
7.2%
J136
 
5.5%
u136
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i500
20.3%
n323
13.1%
o323
13.1%
r323
13.1%
e187
 
7.6%
S187
 
7.6%
M177
 
7.2%
d177
 
7.2%
J136
 
5.5%
u136
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i500
20.3%
n323
13.1%
o323
13.1%
r323
13.1%
e187
 
7.6%
S187
 
7.6%
M177
 
7.2%
d177
 
7.2%
J136
 
5.5%
u136
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i500
20.3%
n323
13.1%
o323
13.1%
r323
13.1%
e187
 
7.6%
S187
 
7.6%
M177
 
7.2%
d177
 
7.2%
J136
 
5.5%
u136
 
5.5%

Code_Complexity
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.3 KiB
Medium
206 
High
155 
Low
139 

Length

Max length6
Median length4
Mean length4.546
Min length3

Characters and Unicode

Total characters2273
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowLow
4th rowLow
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium206
41.2%
High155
31.0%
Low139
27.8%

Length

2026-01-20T17:39:55.734064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T17:39:55.802384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium206
41.2%
high155
31.0%
low139
27.8%

Most occurring characters

ValueCountFrequency (%)
i361
15.9%
M206
9.1%
e206
9.1%
d206
9.1%
u206
9.1%
m206
9.1%
H155
6.8%
g155
6.8%
h155
6.8%
L139
 
6.1%
Other values (2)278
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i361
15.9%
M206
9.1%
e206
9.1%
d206
9.1%
u206
9.1%
m206
9.1%
H155
6.8%
g155
6.8%
h155
6.8%
L139
 
6.1%
Other values (2)278
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i361
15.9%
M206
9.1%
e206
9.1%
d206
9.1%
u206
9.1%
m206
9.1%
H155
6.8%
g155
6.8%
h155
6.8%
L139
 
6.1%
Other values (2)278
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i361
15.9%
M206
9.1%
e206
9.1%
d206
9.1%
u206
9.1%
m206
9.1%
H155
6.8%
g155
6.8%
h155
6.8%
L139
 
6.1%
Other values (2)278
12.2%

Remote_Work
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
255 
True
245 
ValueCountFrequency (%)
False255
51.0%
True245
49.0%
2026-01-20T17:39:55.874879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Stress_Level
Real number (ℝ)

High correlation 

Distinct266
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.004177
Minimum0.49262514
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2026-01-20T17:39:55.969057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.49262514
5-th percentile21.212161
Q156.315125
median94.139876
Q3100
95-th percentile100
Maximum100
Range99.507375
Interquartile range (IQR)43.684875

Descriptive statistics

Standard deviation27.65636
Coefficient of variation (CV)0.35915403
Kurtosis-0.34102688
Mean77.004177
Median Absolute Deviation (MAD)5.8601238
Skewness-0.89856161
Sum38502.088
Variance764.87426
MonotonicityNot monotonic
2026-01-20T17:39:56.103019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100235
47.0%
50.099873231
 
0.2%
67.50076221
 
0.2%
99.504878241
 
0.2%
26.485048311
 
0.2%
16.396672741
 
0.2%
63.233068191
 
0.2%
56.183229471
 
0.2%
99.70009221
 
0.2%
53.057387271
 
0.2%
Other values (256)256
51.2%
ValueCountFrequency (%)
0.49262514131
0.2%
0.84234195461
0.2%
1.2848423551
0.2%
3.2263478461
0.2%
3.3867017411
0.2%
3.764215351
0.2%
4.576906811
0.2%
7.7750874271
0.2%
8.7718056211
0.2%
10.914110191
0.2%
ValueCountFrequency (%)
100235
47.0%
99.70009221
 
0.2%
99.504878241
 
0.2%
98.918570021
 
0.2%
98.755154441
 
0.2%
98.457537991
 
0.2%
97.852729181
 
0.2%
97.556745371
 
0.2%
97.236483051
 
0.2%
96.864980991
 
0.2%

Interactions

2026-01-20T17:39:52.313900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:45.690455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:46.502487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:47.279147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:48.064798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:49.322128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:50.414026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:51.502274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:52.402588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:45.815037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:46.593610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:47.391446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:48.177424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T17:39:51.397285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T17:39:52.222345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-20T17:39:56.211114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BugsCode_ComplexityCoffee_CupsDeadline_DaysExperience_YearsHours_WorkedInterruptionsMeetingsRemote_WorkSleep_HoursStress_Level
Bugs1.0000.034-0.0290.0120.000-0.103-0.061-0.0200.0000.0050.006
Code_Complexity0.0341.0000.0000.0790.0290.0340.0000.1100.0000.0000.115
Coffee_Cups-0.0290.0001.0000.0170.065-0.004-0.065-0.0210.0000.031-0.050
Deadline_Days0.0120.0790.0171.0000.040-0.050-0.0400.0040.0000.044-0.190
Experience_Years0.0000.0290.0650.0401.0000.0000.0990.0260.0000.0460.031
Hours_Worked-0.1030.034-0.004-0.0500.0001.0000.043-0.0140.000-0.0170.229
Interruptions-0.0610.000-0.065-0.0400.0990.0431.000-0.0770.0000.0040.226
Meetings-0.0200.110-0.0210.0040.026-0.014-0.0771.0000.020-0.0800.249
Remote_Work0.0000.0000.0000.0000.0000.0000.0000.0201.0000.0000.023
Sleep_Hours0.0050.0000.0310.0440.046-0.0170.004-0.0800.0001.000-0.726
Stress_Level0.0060.115-0.050-0.1900.0310.2290.2260.2490.023-0.7261.000

Missing values

2026-01-20T17:39:53.361094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-20T17:39:53.481750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Hours_WorkedSleep_HoursBugsDeadline_DaysCoffee_CupsMeetingsInterruptionsExperience_YearsCode_ComplexityRemote_WorkStress_Level
01082553492SeniorMediumYes58.521033
1783333269JuniorMediumYes47.461651
2148445410122JuniorLowNo59.211580
31165460139MidLowYes100.000000
4873623932JuniorMediumYes28.784957
51063253574SeniorMediumNo68.798863
61342121144SeniorHighNo100.000000
764204203MidMediumYes100.000000
8107555030JuniorMediumYes42.817044
9146532824JuniorMediumNo46.183850
Hours_WorkedSleep_HoursBugsDeadline_DaysCoffee_CupsMeetingsInterruptionsExperience_YearsCode_ComplexityRemote_WorkStress_Level
4906828286210JuniorLowYes13.926934
49115420208126MidHighNo100.000000
49213310460106MidMediumYes100.000000
49366391510160SeniorMediumNo24.310410
4948510366123SeniorLowYes70.221640
495943558751MidLowYes100.000000
49612348192161MidLowNo100.000000
4971573853121JuniorMediumYes87.110845
4988839189183MidLowYes51.088981
4994634565200MidHighYes22.977132